Automated fault analysis and diagnosis using high-frequency and maintenance data from distribution networks

Research output: Contribution to conferencePaper

Abstract

Fault analysis based on high-resolution data acquisition is growing in use as it offers a more complete picture of faults which provides an opportunity to deal with failures more effectively. However, with increased volume of data collected, it becomes impossible for engineers to interpret every fault instance. A machine learning approach to classification should be the solution to this, but it is time-consuming to manually label faults for training and validation making data-driven approaches impossible to transfer into practical implementation. A solution to this is to unify fault analysis with maintenance report analysis to automate the generation of training labels. This paper outlines how a fully automatic fault detection and diagnostic approach based around power quality waveform analysis can be used to improve situational awareness on distribution networks. The methodology is illustrated using operational case study data and realistic simulations to demonstrate the diagnostic functionality as well as the practical benefit. In particular, classification accuracy is shown to approach that of expert labelled fault data.

Conference

ConferenceIEEE PES Innovative Smart Grid Technologies Conference Europe 2019
Abbreviated titleISGT-E
CountryRomania
CityBucharest
Period29/09/192/10/19
Internet address

Fingerprint

Electric power distribution
Labels
Waveform analysis
Power quality
Fault detection
Learning systems
Data acquisition
Engineers

Keywords

  • power quality
  • automatic fault analysis
  • increasing situational awareness
  • high-resolution monitoring
  • fault labelling

Cite this

Jiang, X., Stephen, B., & McArthur, S. (2019). Automated fault analysis and diagnosis using high-frequency and maintenance data from distribution networks. Paper presented at IEEE PES Innovative Smart Grid Technologies Conference Europe 2019, Bucharest, Romania.
Jiang, Xu ; Stephen, Bruce ; McArthur, Stephen. / Automated fault analysis and diagnosis using high-frequency and maintenance data from distribution networks. Paper presented at IEEE PES Innovative Smart Grid Technologies Conference Europe 2019, Bucharest, Romania.
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title = "Automated fault analysis and diagnosis using high-frequency and maintenance data from distribution networks",
abstract = "Fault analysis based on high-resolution data acquisition is growing in use as it offers a more complete picture of faults which provides an opportunity to deal with failures more effectively. However, with increased volume of data collected, it becomes impossible for engineers to interpret every fault instance. A machine learning approach to classification should be the solution to this, but it is time-consuming to manually label faults for training and validation making data-driven approaches impossible to transfer into practical implementation. A solution to this is to unify fault analysis with maintenance report analysis to automate the generation of training labels. This paper outlines how a fully automatic fault detection and diagnostic approach based around power quality waveform analysis can be used to improve situational awareness on distribution networks. The methodology is illustrated using operational case study data and realistic simulations to demonstrate the diagnostic functionality as well as the practical benefit. In particular, classification accuracy is shown to approach that of expert labelled fault data.",
keywords = "power quality, automatic fault analysis, increasing situational awareness, high-resolution monitoring, fault labelling",
author = "Xu Jiang and Bruce Stephen and Stephen McArthur",
note = "{\circledC} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; IEEE PES Innovative Smart Grid Technologies Conference Europe 2019, ISGT-E ; Conference date: 29-09-2019 Through 02-10-2019",
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month = "9",
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language = "English",
url = "http://sites.ieee.org/isgt-europe-2019/",

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Jiang, X, Stephen, B & McArthur, S 2019, 'Automated fault analysis and diagnosis using high-frequency and maintenance data from distribution networks' Paper presented at IEEE PES Innovative Smart Grid Technologies Conference Europe 2019, Bucharest, Romania, 29/09/19 - 2/10/19, .

Automated fault analysis and diagnosis using high-frequency and maintenance data from distribution networks. / Jiang, Xu; Stephen, Bruce; McArthur, Stephen.

2019. Paper presented at IEEE PES Innovative Smart Grid Technologies Conference Europe 2019, Bucharest, Romania.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Automated fault analysis and diagnosis using high-frequency and maintenance data from distribution networks

AU - Jiang, Xu

AU - Stephen, Bruce

AU - McArthur, Stephen

N1 - © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2019/9/29

Y1 - 2019/9/29

N2 - Fault analysis based on high-resolution data acquisition is growing in use as it offers a more complete picture of faults which provides an opportunity to deal with failures more effectively. However, with increased volume of data collected, it becomes impossible for engineers to interpret every fault instance. A machine learning approach to classification should be the solution to this, but it is time-consuming to manually label faults for training and validation making data-driven approaches impossible to transfer into practical implementation. A solution to this is to unify fault analysis with maintenance report analysis to automate the generation of training labels. This paper outlines how a fully automatic fault detection and diagnostic approach based around power quality waveform analysis can be used to improve situational awareness on distribution networks. The methodology is illustrated using operational case study data and realistic simulations to demonstrate the diagnostic functionality as well as the practical benefit. In particular, classification accuracy is shown to approach that of expert labelled fault data.

AB - Fault analysis based on high-resolution data acquisition is growing in use as it offers a more complete picture of faults which provides an opportunity to deal with failures more effectively. However, with increased volume of data collected, it becomes impossible for engineers to interpret every fault instance. A machine learning approach to classification should be the solution to this, but it is time-consuming to manually label faults for training and validation making data-driven approaches impossible to transfer into practical implementation. A solution to this is to unify fault analysis with maintenance report analysis to automate the generation of training labels. This paper outlines how a fully automatic fault detection and diagnostic approach based around power quality waveform analysis can be used to improve situational awareness on distribution networks. The methodology is illustrated using operational case study data and realistic simulations to demonstrate the diagnostic functionality as well as the practical benefit. In particular, classification accuracy is shown to approach that of expert labelled fault data.

KW - power quality

KW - automatic fault analysis

KW - increasing situational awareness

KW - high-resolution monitoring

KW - fault labelling

M3 - Paper

ER -

Jiang X, Stephen B, McArthur S. Automated fault analysis and diagnosis using high-frequency and maintenance data from distribution networks. 2019. Paper presented at IEEE PES Innovative Smart Grid Technologies Conference Europe 2019, Bucharest, Romania.